About: A Review of Multi‐Compartment Infectious Disease Models

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A Review of Multi‐Compartment Infectious Disease Models
Creator	Wang, Lili He, Jie Tang, Lu Wang, Fei Zhou, Yiwang Purkayastha, Soumik Song, -K Zhang, Leyao
Source	Medline; PMC
abstract	Multi‐compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community‐level micromodel that enables high‐resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper.
has issue date	2020-08-03(xsd:dateTime)
bibo:doi	10.1111/insr.12402
bibo:pmid	32834402
has license	no-cc
sha1sum (hex)	62c23f3f578d475604de3d6dc5c4cd671a745e8b
schema:url	https://doi.org/10.1111/insr.12402
resource representing a document's title	A Review of Multi‐Compartment Infectious Disease Models
has PubMed Central identifier	PMC7436714
has PubMed identifier	32834402
schema:publication	Int Stat Rev
resource representing a document's body	covid:62c23f3f578d475604de3d6dc5c4cd671a745e8b#body_text
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